APOE_finemapping
Brian M. Schilder
Most recent update:
2021-03-22
knitr::opts_chunk$set(echo = T, root.dir="~/Desktop/APOE_finemapping")
knitr::opts_knit$set(root.dir="~/Desktop/APOE_finemapping")
library(dplyr)
library(ggplot2)
library(echolocatoR)Import fine-mapping results
Query fine-mapping portal
Download Alzheimer’s Disease GWAS fine-mapping results for the APOE locus via the echolocatoR Fine-mapping Portal API.
local_finemap <- echolocatoR::GITHUB.portal_query(dataset_types = "GWAS",
phenotypes = "Alzheimer",
LD_panels = c("UKB", "1KGphase3"),
loci = "APOE",
file_types = "multi_finemap",
results_dir = "./data",
overwrite = F)## [1] "Fetching echolocatoR Fine-mapping Portal study metadata."
## [1] "+ 4 datasets remain after filtering."
## [1] "+ Searching for multi_finemap files..."
## [1] "+ 2 unique files identified."
## [1] "+ Downloading 2 files..."
## [1] "+ Returning local file paths."Merge fine-mapping results
finemap_dat <- lapply(local_finemap,function(x){
dataset <- basename(dirname(dirname(dirname(x))))
LD_ref <- stringr::str_split(basename(x),pattern = "[.]")[[1]][2]
printer("Importing",dataset)
dat <- data.table::fread(x)
cbind(dataset=dataset,
LD_ref=LD_ref,
dat)
}) %>% data.table::rbindlist()## [1] "Importing Jansen_2018"
## [1] "Importing Marioni_2018"Identify credible sets
Show just the Union Credible Set SNPs (SNPs nominated by at least 1/4 fine-mapping tools).
ucs_snps <- subset(finemap_dat, Support>0)
createDT(ucs_snps)Identify Consensus SNPs
Filter to just the consensus SNPs (SNPs nominated by 2 or more fine-mapping tools).
consensus_snps <- subset(finemap_dat, Consensus_SNP==T)
createDT(consensus_snps)Cell-type-specific annotations
Epigenomic peak overlap
finemap_dat$Gene<- finemap_dat$Locus
finemap_dat$Locus <- paste(finemap_dat$Locus,finemap_dat$dataset,sep="_")
plot_res <- echolocatoR::SUMMARISE.peak_overlap_plot(merged_DT = finemap_dat,
snp_filter = "Support>0",
include.NOTT_2019_peaks = T,
include.NOTT_2019_enhancers_promoters = T,
include.NOTT_2019_PLACseq = T,
include.CORCES_2020_scATACpeaks = T,
include.CORCES_2020_Cicero_coaccess = T,
include.CORCES_2020_bulkATACpeaks = T,
include.CORCES_2020_HiChIP_FitHiChIP_coaccess = T,
include.CORCES_2020_gene_annotations = T,
plot_celltype_specificity = T,
plot_celltype_specificity_genes = T)## [1] "++ NOTT_2019:: Downloading and merging 12 peaks BED files."
## [1] "++ NOTT_2019:: Converting merged BED files to GRanges."
## [1] "++ NOTT_2019:: 634540 ranges retrieved."
## [1] "Importing Astrocyte enhancers ..."
## [1] "Importing Astrocyte promoters ..."
## [1] "Importing Neuronal enhancers ..."
## [1] "Importing Neuronal promoters ..."
## [1] "Importing Oligo enhancers ..."
## [1] "Importing Oligo promoters ..."
## [1] "Importing Microglia enhancers ..."
## [1] "Importing Microglia promoters ..."
## [1] "++ Converting to GRanges."
## [1] "Importing Microglia interactome ..."
## [1] "Importing Neuronal interactome ..."
## [1] "Importing Oligo interactome ..."
## [1] "CORCES_2020:: Extracting overlapping cell-type-specific scATAC-seq peaks"
## Error in `[[<-`(`*tmp*`, name, value = "scATAC") :
## 1 elements in value to replace 0 elements
## [1] "CORCES_2020:: Extracting overlapping bulkATAC-seq peaks from brain tissue"
## Error in `[[<-`(`*tmp*`, name, value = "bulkATAC") :
## 1 elements in value to replace 0 elements
Show the epigenomic overlap results
createDT(plot_res$data)Summarise assay counts
Summarise the number of assays in which at least one UCS SNP overlapped with a cell-type-specific epigenomic peak.
celltype_counts <- plot_res$data %>%
dplyr::group_by(Locus, Cell_type) %>%
dplyr::summarise(Count=sum(Count,na.rm = T)) %>%
dplyr::arrange(Locus, desc(Count))## `summarise()` has grouped output by 'Locus'. You can override using the `.groups` argument.createDT(celltype_counts)SNP-level results
The above reports are useful to see thing in aggregate, but here we look at the SNP-level results.
gr.hits <- SUMMARISE.peak_overlap(merged_DT=finemap_dat,
snp_filter="Support>0",
include.NOTT_2019_peaks=T,
include.NOTT_2019_enhancers_promoters=T,
include.NOTT_2019_PLACseq=T,
include.CORCES_2020_scATACpeaks=T,
include.CORCES_2020_Cicero_coaccess=T,
include.CORCES_2020_bulkATACpeaks=T,
include.CORCES_2020_HiChIP_FitHiChIP_coaccess=T,
include.CORCES_2020_gene_annotations=T)## [1] "++ NOTT_2019:: Downloading and merging 12 peaks BED files."
## [1] "++ NOTT_2019:: Converting merged BED files to GRanges."
## [1] "++ NOTT_2019:: 634540 ranges retrieved."## 10 query SNP(s) detected with reference overlap.## [1] "Importing Astrocyte enhancers ..."
## [1] "Importing Astrocyte promoters ..."
## [1] "Importing Neuronal enhancers ..."
## [1] "Importing Neuronal promoters ..."
## [1] "Importing Oligo enhancers ..."
## [1] "Importing Oligo promoters ..."
## [1] "Importing Microglia enhancers ..."
## [1] "Importing Microglia promoters ..."
## [1] "++ Converting to GRanges."## 16 query SNP(s) detected with reference overlap.## [1] "Importing Microglia interactome ..."
## [1] "Importing Neuronal interactome ..."
## [1] "Importing Oligo interactome ..."## 41 query SNP(s) detected with reference overlap.## 13 query SNP(s) detected with reference overlap.## [1] "CORCES_2020:: Extracting overlapping cell-type-specific scATAC-seq peaks"## Start at 2021-03-22 16:01:04## ##
## End at 2021-03-22 16:01:06## Runtime in total is: 2 secs## 0 query SNP(s) detected with reference overlap.## Error in `[[<-`(`*tmp*`, name, value = "scATAC") :
## 1 elements in value to replace 0 elements
## [1] "CORCES_2020:: Extracting overlapping bulkATAC-seq peaks from brain tissue"## Start at 2021-03-22 16:01:06## ##
## End at 2021-03-22 16:01:08## Runtime in total is: 2 secs## 0 query SNP(s) detected with reference overlap.## Error in `[[<-`(`*tmp*`, name, value = "bulkATAC") :
## 1 elements in value to replace 0 elements
## [1] "80 hits across 4 assays in 1 studies found."snp_overlap_counts <- data.frame(gr.hits) %>%
dplyr::group_by(Locus, SNP, Cell_type, Consensus_SNP, Support) %>%
dplyr::summarise(Assay_count=n()) %>%
dplyr::arrange(desc(Consensus_SNP), desc(Support), desc(Assay_count))## `summarise()` has grouped output by 'Locus', 'SNP', 'Cell_type', 'Consensus_SNP'. You can override using the `.groups` argument.createDT(snp_overlap_counts)Colocalization
Import colocalization results from MiGA preprint:
Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies
Katia de Paiva Lopes, Gijsje J. L. Snijders, Jack Humphrey, Amanda Allan, Marjolein Sneeboer, Elisa Navarro, Brian M. Schilder, Ricardo A. Vialle, Madison Parks, Roy Missall, Welmoed van Zuiden, Frederieke Gigase, Raphael Kübler, Amber Berdenis van Berlekom, Chotima Böttcher, Josef Priller, René S. Kahn, Lot D. de Witte, Towfique Raj
bioRxiv 2020.10.27.356113; doi: https://doi.org/10.1101/2020.10.27.356113
AD coloc results: all loci
coloc_res <- data.table::fread(here::here("data/MiGA/all_COLOC_results.Microglia_all_regions.snp-level.leadSNP.PPH4_filtered.tsv.gz"))
meta <- echolocatoR::GITHUB.portal_metadata()## [1] "Fetching echolocatoR Fine-mapping Portal study metadata."AD_studies <- subset(meta, phenotype=="Alzheimer's Disease")$dataset
coloc_AD <- subset(coloc_res, Study %in% AD_studies)
dim(coloc_AD)## [1] 42 43createDT(coloc_AD)AD coloc results: APOE locus
No genes in microglia eQTL are colocalized with the APOE locus in AD GWAS at PP.H4>0.8.
subset(coloc_AD, Locus=="APOE")## Empty data.table (0 rows and 43 cols): Study,Dataset,Locus,Gene,snp,V.df1...Let’s instead ask what the top colocalized gene in the APOE locus is per study.
Using this approach, the top eQTL gene is FKRP.
coloc_res <- data.table::fread(here::here("data/MiGA/all_COLOC_results.Microglia_all_regions.snp-level.leadSNP.tsv.gz"))
top_coloc <- subset(coloc_res, Study %in% AD_studies & Locus=="APOE") %>%
dplyr::group_by(Study, Locus) %>%
dplyr::slice_max(SNP.PP.H4, n = 1)
createDT(top_coloc)CatalogueR
Microglia do seem to be quite important in AD, but the microglia-specific QTL studies are currently available are underpowered. Let’s take a different strategy and query the eQTL Catalogue via catalogueR.
library(catalogueR)Jansen_2018
gwas.qtl_paths.Jansen_2018 <- catalogueR::eQTL_Catalogue.query(sumstats_paths = local_finemap[1],
output_dir = "data/catalogueR_queries_Jansen_2018",
split_files = T,
qtl_search = c("brain","monocyte","macrophage"),
conda_env ="echoR",
nThread = 10)Marioni_2018
gwas.qtl_paths.Marioni_2018 <- catalogueR::eQTL_Catalogue.query(sumstats_paths = local_finemap[2],
output_dir = "data/catalogueR_queries_Marioni_2018",
split_files = T,
qtl_search = c("brain","monocyte","macrophage"),
conda_env ="echoR",
force_new_subset = F,
nThread = 10)Run coloc
Get sample sizes
local_meta <- "data/GWAS-QTL_data_dictionary.xlsx"
if(!file.exists(local_meta)){
download.file("https://github.com/RajLabMSSM/Fine_Mapping_Shiny/raw/master/www/metadata/GWAS-QTL_data_dictionary.xlsx",destfile = local_meta)
}
meta2 <- xlsx::read.xlsx2(here::here("data/GWAS-QTL_data_dictionary.xlsx"), sheetName = "GWAS")
meta2$N <- as.numeric(meta2$N)Jansen_2018
# Gather paths
gwas.qtl_paths.Jansen_2018 <- list.files("data/catalogueR_queries_Jansen_2018/",
pattern = ".tsv.gz",
recursive = T, full.names = T)
coloc_QTLs.Jansen_2018 <- catalogueR::run_coloc(gwas.qtl_paths = gwas.qtl_paths.Jansen_2018,
save_path = "data/catalogueR_queries_Jansen_2018/coloc.Jansen_2018.tsv.gz",
gwas_sample_size = subset(meta2, dataset=="Jansen_2018")$N,
nThread = 10)Marioni_2018
# Gather paths
gwas.qtl_paths.Marioni_2018 <- list.files("data/catalogueR_queries_Marioni_2018/",
pattern = ".tsv.gz",
recursive = T, full.names = T)
coloc_QTLs.Marioni_2018 <- catalogueR::run_coloc(gwas.qtl_paths = gwas.qtl_paths.Marioni_2018,
save_path = "data/catalogueR_queries_Marioni_2018/coloc.Jansen_2018.tsv.gz",
gwas_sample_size = subset(meta2, dataset=="Marioni_2018")$N,
nThread = 10)Merge results
coloc_QTLs.Jansen_2018 <- data.table::fread(here::here("data/catalogueR_queries_Jansen_2018/coloc.Jansen_2018.tsv.gz"), nThread = 10)
coloc_QTLs.Marioni_2018 <- data.table::fread(here::here("data/catalogueR_queries_Marioni_2018/coloc.Marioni_2018.tsv.gz"), nThread = 10)
coloc_res <- rbind(cbind(dataset="Jansen_2018",
coloc_QTLs.Jansen_2018),
cbind(dataset="Marioni_2018",
coloc_QTLs.Marioni_2018), fill=T)
top_coloc <- coloc_res %>%
dplyr::select(dataset, Locus.GWAS, PP.H4, snp, SNP.PP.H4, qtl_id, gene.QTL) %>%
unique() %>%
subset(PP.H4>0.5) %>%
dplyr::group_by(dataset, Locus.GWAS, snp) %>%
dplyr::slice_max(order_by = PP.H4, n=3 )
createDT(top_coloc)Fine-mapped QTLs
The eQTL Catalogue team recently fine-mapped their entire database (using the tool FINEMAP). We can check for overlap between causal variants here.
Problem : None of the Consensus or Support>0 SNPs seem to be present in the eQTL Catalogue fine-mapping data (filtering choices before fine-mapping?). Thus, this approach isn’t very helpful in this case.
library(GenomicRanges)ftp_path <- file.path("ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/credible_sets",
"BLUEPRINT_SE.monocyte_ge.purity_filtered.txt.gz"
# "GTEx.brain_cortex_ge.purity_filtered.txt.gz"
)
ftp_cs <- data.table::fread(ftp_path, nThread = 10)
gr.cs <- subset(ftp_cs, pip>0) %>%
GenomicRanges::makeGRangesFromDataFrame(keep.extra.columns = T,
seqnames.field = "chr",
start.field = "pos", end.field = "pos")
# catalogueR:::rsids_from_coords(gr.cs, genome_build = "hg38")
gr.finemap <- catalogueR::liftover(gwas_data = finemap_dat,
build.conversion = "hg19.to.hg38")## [1] "XGR:: Lifting genome build: hg19.to.hg38"## Start at 2021-03-22 16:01:53## ## First, import the files formatted as 'GRanges' (2021-03-22 16:01:53) ...## import the data file (2021-03-22 16:01:53) ...## Second, construct GenomicRanges object (2021-03-22 16:01:53) ...## Third, lift intervals between genome builds 'hg19.to.hg38' (2021-03-22 16:01:53) ...## Start at 2021-03-22 16:01:53## ## 'chain' (from http://galahad.well.ox.ac.uk/bigdata/chain.RData) has been loaded into the working environment (at 2021-03-22 16:01:54)##
## End at 2021-03-22 16:01:54## Runtime in total is: 1 secs##
## End at 2021-03-22 16:01:54## Runtime in total is: 1 secssuppressWarnings(GenomeInfoDb::seqlevelsStyle(gr.finemap) <- "NCBI")
#### Find any eQTL Catalogue CS within the range ####
gr.range <- subset(gr.cs,
(seqnames(gr.cs)==19) &
(start(gr.cs)>=min(start(gr.finemap))) &
(end(gr.cs)<=max(start(gr.finemap))))
gene_dict <- catalogueR::ensembl_to_hgnc(gr.range$phenotype_id)## [1] "++ Converting: Ensembl IDs ==> HGNC gene symbols"gr.range$gene <- gene_dict[gr.range$phenotype_id]
#### Find overlap in causal SNPs ####
gr.hits <- echolocatoR::GRanges_overlap(gr.finemap, gr.cs)## 698 query SNP(s) detected with reference overlap.gene_dict <- catalogueR::ensembl_to_hgnc(gr.hits$phenotype_id)## [1] "++ Converting: Ensembl IDs ==> HGNC gene symbols"gr.hits$gene <- gene_dict[gr.hits$phenotype_id]Summarise
Merge the nominated cell-types (from epigenomic overlap) with the nominated genes (from colocalization) by SNPs.
Top candidates:
Cell-type: microglia
Gene: FOSB
SNP: rs11556505
rs11556505 has previously been linked to AD and hippocampal degradation.
This SNP falls within an exon of TOMM40, which is a gene immediately next to APOE.
Colocalization analyses nominated FOSB instead, which is ~500Mb+ downstream of TOMM40.
and was shown to be somewhat correlated with HC volume and free-recall memory in humans. However they attributed the effect of rs11556505 to TOMM40.
The influence of APOE and TOMM40 polymorphisms on hippocampal volume and episodic memory in old age, https://www.frontiersin.org/articles/10.3389/fnhum.2013.00198/full
candidates <- merge(snp_overlap_counts,
top_coloc,
all.y = T,
by.x = "SNP", by.y = "snp")
createDT(candidates) Session info
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.4 (2021-02-15)
## os macOS Big Sur 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_GB.UTF-8
## ctype en_GB.UTF-8
## tz Europe/London
## date 2021-03-22
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## AnnotationDbi 1.52.0 2020-10-27 [1] Bioconductor
## AnnotationFilter 1.14.0 2020-10-27 [1] Bioconductor
## ape 5.4-1 2020-08-13 [1] CRAN (R 4.0.2)
## askpass 1.1 2019-01-13 [1] CRAN (R 4.0.2)
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
## backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.2)
## base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.0.2)
## Biobase 2.50.0 2020-10-27 [1] Bioconductor
## BiocFileCache 1.14.0 2020-10-27 [1] Bioconductor
## BiocGenerics * 0.36.0 2020-10-27 [1] Bioconductor
## BiocManager 1.30.10 2019-11-16 [1] CRAN (R 4.0.2)
## BiocParallel 1.24.1 2020-11-06 [1] Bioconductor
## biomaRt 2.46.3 2021-02-11 [1] Bioconductor
## Biostrings 2.58.0 2020-10-27 [1] Bioconductor
## biovizBase 1.38.0 2020-10-27 [1] Bioconductor
## bit 4.0.4 2020-08-04 [1] CRAN (R 4.0.2)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.0.2)
## bitops 1.0-6 2013-08-17 [1] CRAN (R 4.0.2)
## blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.2)
## boot 1.3-27 2021-02-12 [1] CRAN (R 4.0.2)
## BSgenome 1.58.0 2020-10-27 [1] Bioconductor
## bslib 0.2.4 2021-01-25 [1] CRAN (R 4.0.2)
## cachem 1.0.4 2021-02-13 [1] CRAN (R 4.0.2)
## catalogueR * 0.1.0 2021-03-20 [1] local
## checkmate 2.0.0 2020-02-06 [1] CRAN (R 4.0.2)
## class 7.3-18 2021-01-24 [1] CRAN (R 4.0.4)
## cli 2.3.1 2021-02-23 [1] CRAN (R 4.0.2)
## cluster 2.1.1 2021-02-14 [1] CRAN (R 4.0.2)
## colorspace 2.0-0 2020-11-11 [1] CRAN (R 4.0.2)
## crayon 1.4.1 2021-02-08 [1] CRAN (R 4.0.2)
## crosstalk 1.1.1 2021-01-12 [1] CRAN (R 4.0.2)
## curl 4.3 2019-12-02 [1] CRAN (R 4.0.1)
## data.table 1.13.6 2020-12-30 [1] CRAN (R 4.0.4)
## DBI 1.1.1 2021-01-15 [1] CRAN (R 4.0.2)
## dbplyr 2.1.0 2021-02-03 [1] CRAN (R 4.0.2)
## DelayedArray 0.16.2 2021-02-26 [1] Bioconductor
## DescTools 0.99.40 2021-02-03 [1] CRAN (R 4.0.2)
## dichromat 2.0-0 2013-01-24 [1] CRAN (R 4.0.2)
## digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.2)
## dnet 1.1.7 2020-02-20 [1] CRAN (R 4.0.2)
## dplyr * 1.0.5 2021-03-05 [1] CRAN (R 4.0.2)
## DT 0.17 2021-01-06 [1] CRAN (R 4.0.2)
## e1071 1.7-4 2020-10-14 [1] CRAN (R 4.0.2)
## echolocatoR * 0.1.2 2021-03-20 [1] local
## ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2)
## EnsDb.Hsapiens.v75 2.99.0 2021-03-13 [1] Bioconductor
## ensembldb 2.14.0 2020-10-27 [1] Bioconductor
## evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.1)
## Exact 2.1 2020-10-02 [1] CRAN (R 4.0.2)
## expm 0.999-6 2021-01-13 [1] CRAN (R 4.0.2)
## fansi 0.4.2 2021-01-15 [1] CRAN (R 4.0.2)
## farver 2.1.0 2021-02-28 [1] CRAN (R 4.0.2)
## fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.0.2)
## foreign 0.8-81 2020-12-22 [1] CRAN (R 4.0.4)
## Formula 1.2-4 2020-10-16 [1] CRAN (R 4.0.2)
## generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.2)
## GenomeInfoDb * 1.26.4 2021-03-10 [1] Bioconductor
## GenomeInfoDbData 1.2.4 2021-02-10 [1] Bioconductor
## GenomicAlignments 1.26.0 2020-10-27 [1] Bioconductor
## GenomicFeatures 1.42.2 2021-03-12 [1] Bioconductor
## GenomicRanges * 1.42.0 2020-10-27 [1] Bioconductor
## GGally 2.1.1 2021-03-08 [1] CRAN (R 4.0.2)
## ggbio 1.38.0 2020-10-27 [1] Bioconductor
## ggnetwork 0.5.8 2020-02-12 [1] CRAN (R 4.0.2)
## ggplot2 * 3.3.3 2020-12-30 [1] CRAN (R 4.0.2)
## ggrepel 0.9.1 2021-01-15 [1] CRAN (R 4.0.2)
## gld 2.6.2 2020-01-08 [1] CRAN (R 4.0.2)
## glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
## graph 1.68.0 2020-10-27 [1] Bioconductor
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.0.2)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
## here 1.0.1 2020-12-13 [1] CRAN (R 4.0.2)
## hexbin 1.28.2 2021-01-08 [1] CRAN (R 4.0.2)
## highr 0.8 2019-03-20 [1] CRAN (R 4.0.2)
## Hmisc 4.5-0 2021-02-28 [1] CRAN (R 4.0.2)
## hms 1.0.0 2021-01-13 [1] CRAN (R 4.0.2)
## htmlTable 2.1.0 2020-09-16 [1] CRAN (R 4.0.2)
## htmltools 0.5.1.1 2021-01-22 [1] CRAN (R 4.0.2)
## htmlwidgets 1.5.3 2020-12-10 [1] CRAN (R 4.0.2)
## httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
## igraph 1.2.6 2020-10-06 [1] CRAN (R 4.0.2)
## IRanges * 2.24.1 2020-12-12 [1] Bioconductor
## jpeg 0.1-8.1 2019-10-24 [1] CRAN (R 4.0.2)
## jquerylib 0.1.3 2020-12-17 [1] CRAN (R 4.0.2)
## jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.0.2)
## knitr 1.31 2021-01-27 [1] CRAN (R 4.0.2)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.0.2)
## lattice 0.20-41 2020-04-02 [1] CRAN (R 4.0.4)
## latticeExtra 0.6-29 2019-12-19 [1] CRAN (R 4.0.2)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.0.2)
## lifecycle 1.0.0 2021-02-15 [1] CRAN (R 4.0.3)
## lmom 2.8 2019-03-12 [1] CRAN (R 4.0.2)
## magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.0.2)
## MASS 7.3-53.1 2021-02-12 [1] CRAN (R 4.0.2)
## Matrix 1.3-2 2021-01-06 [1] CRAN (R 4.0.4)
## MatrixGenerics 1.2.1 2021-01-30 [1] Bioconductor
## matrixStats 0.58.0 2021-01-29 [1] CRAN (R 4.0.2)
## memoise 2.0.0 2021-01-26 [1] CRAN (R 4.0.2)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
## mvtnorm 1.1-1 2020-06-09 [1] CRAN (R 4.0.2)
## nlme 3.1-152 2021-02-04 [1] CRAN (R 4.0.4)
## nnet 7.3-15 2021-01-24 [1] CRAN (R 4.0.4)
## openssl 1.4.3 2020-09-18 [1] CRAN (R 4.0.2)
## OrganismDbi 1.32.0 2020-10-27 [1] Bioconductor
## patchwork * 1.1.1 2020-12-17 [1] CRAN (R 4.0.2)
## pillar 1.5.1 2021-03-05 [1] CRAN (R 4.0.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
## plyr 1.8.6 2020-03-03 [1] CRAN (R 4.0.2)
## png 0.1-7 2013-12-03 [1] CRAN (R 4.0.2)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
## progress 1.2.2 2019-05-16 [1] CRAN (R 4.0.2)
## ProtGenerics 1.22.0 2020-10-27 [1] Bioconductor
## purrr 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
## R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.0.2)
## R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.0.2)
## R.utils 2.10.1 2020-08-26 [1] CRAN (R 4.0.2)
## R6 2.5.0 2020-10-28 [1] CRAN (R 4.0.2)
## rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.0.2)
## RBGL 1.66.0 2020-10-27 [1] Bioconductor
## RCircos 1.2.1 2019-03-12 [1] CRAN (R 4.0.2)
## RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 4.0.2)
## Rcpp 1.0.6 2021-01-15 [1] CRAN (R 4.0.2)
## RCurl 1.98-1.2 2020-04-18 [1] CRAN (R 4.0.2)
## readr 1.4.0 2020-10-05 [1] CRAN (R 4.0.2)
## reshape 0.8.8 2018-10-23 [1] CRAN (R 4.0.2)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.0.2)
## Rgraphviz 2.34.0 2020-10-27 [1] Bioconductor
## rJava 0.9-13 2020-07-06 [1] CRAN (R 4.0.2)
## rlang 0.4.10 2020-12-30 [1] CRAN (R 4.0.2)
## rmarkdown 2.7 2021-02-19 [1] CRAN (R 4.0.3)
## rootSolve 1.8.2.1 2020-04-27 [1] CRAN (R 4.0.2)
## rpart 4.1-15 2019-04-12 [1] CRAN (R 4.0.4)
## rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.2)
## Rsamtools 2.6.0 2020-10-27 [1] Bioconductor
## RSQLite 2.2.4 2021-03-12 [1] CRAN (R 4.0.3)
## rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.0.2)
## rtracklayer 1.50.0 2020-10-27 [1] Bioconductor
## S4Vectors * 0.28.1 2020-12-09 [1] Bioconductor
## sass 0.3.1 2021-01-24 [1] CRAN (R 4.0.2)
## scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2)
## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2)
## stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
## stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
## SummarizedExperiment 1.20.0 2020-10-27 [1] Bioconductor
## supraHex 1.28.1 2020-11-24 [1] Bioconductor
## survival 3.2-7 2020-09-28 [1] CRAN (R 4.0.4)
## tibble 3.1.0 2021-02-25 [1] CRAN (R 4.0.2)
## tidyr 1.1.3 2021-03-03 [1] CRAN (R 4.0.2)
## tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2)
## utf8 1.2.1 2021-03-12 [1] CRAN (R 4.0.3)
## VariantAnnotation 1.36.0 2020-10-28 [1] Bioconductor
## vctrs 0.3.6 2020-12-17 [1] CRAN (R 4.0.2)
## viridisLite 0.3.0 2018-02-01 [1] CRAN (R 4.0.1)
## withr 2.4.1 2021-01-26 [1] CRAN (R 4.0.2)
## xfun 0.22 2021-03-11 [1] CRAN (R 4.0.3)
## XGR 1.1.7 2020-01-08 [1] url
## xlsx 0.6.5 2020-11-10 [1] CRAN (R 4.0.2)
## xlsxjars 0.6.1 2014-08-22 [1] CRAN (R 4.0.2)
## XML 3.99-0.5 2020-07-23 [1] CRAN (R 4.0.2)
## xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.2)
## XVector 0.30.0 2020-10-28 [1] Bioconductor
## yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
## zlibbioc 1.36.0 2020-10-28 [1] Bioconductor
##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library